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      KCI등재 SCIE SCOPUS

      Improving Video Quality by Diversification of Adaptive Streaming Strategies = Improving Video Quality by Diversification of Adaptive Streaming Strategies

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      https://www.riss.kr/link?id=A103334659

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      다국어 초록 (Multilingual Abstract)

      Users quite often experience volatile channel conditions which negatively influence multi-media transmission. HTTP adaptive streaming has emerged as a new promising technology where the video quality can be adjusted to variable network conditions. Nevertheless, the new technology does not remain without drawbacks. As it has been observed, multiple video players sharing the same network link have often problems with achieving good efficiency and stability of play-out due to a mutual interference and competition among video players.
      Our investigation indicates that there may be another cause for under-performance of the streamed video. In an emulated environment, we implemented three algorithms of adaptive video play-out based on bandwidth or buffer assessment. As we show, traffic generated by players employing the same or similar play-out strategies is positively correlated and syn-chronised (clustered), whereas traffic originated from different play-out strategies shows negative or no correlations. However, when some of the parameters of the play-out strategies are randomised, the correlation and synchronisation diminish what has a positive impact on the smoothness of the traffic and on the video quality perceived by end users. Our research shows that non-correlated traffic flows generated by play-out strategies improve efficiency and stability of streamed adaptive video.
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      Users quite often experience volatile channel conditions which negatively influence multi-media transmission. HTTP adaptive streaming has emerged as a new promising technology where the video quality can be adjusted to variable network conditions. Nev...

      Users quite often experience volatile channel conditions which negatively influence multi-media transmission. HTTP adaptive streaming has emerged as a new promising technology where the video quality can be adjusted to variable network conditions. Nevertheless, the new technology does not remain without drawbacks. As it has been observed, multiple video players sharing the same network link have often problems with achieving good efficiency and stability of play-out due to a mutual interference and competition among video players.
      Our investigation indicates that there may be another cause for under-performance of the streamed video. In an emulated environment, we implemented three algorithms of adaptive video play-out based on bandwidth or buffer assessment. As we show, traffic generated by players employing the same or similar play-out strategies is positively correlated and syn-chronised (clustered), whereas traffic originated from different play-out strategies shows negative or no correlations. However, when some of the parameters of the play-out strategies are randomised, the correlation and synchronisation diminish what has a positive impact on the smoothness of the traffic and on the video quality perceived by end users. Our research shows that non-correlated traffic flows generated by play-out strategies improve efficiency and stability of streamed adaptive video.

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      참고문헌 (Reference)

      1 YouTube, "YouTube Statistics"

      2 R. C. L. Gámez, "Wireless network delay estimation for time-sensitive applications" Autom Control Dept Tech. Univ Catalonia Catalonia Spain 2006

      3 S. Akhshabi, "What Happens When HTTP Adaptive Streaming Players Compete for Bandwidth?" 2012

      4 T. Y. Huang, "Using the Buffer to Avoid Rebuffers: Evidence from a Large Video Streaming Service"

      5 F. Wamser, "Using buffered playtime for QoE-oriented resource management of YouTube video streaming" 24 (24): 288-302, 2013

      6 L. Qiu, "Understanding the performance of many TCP flows" 37 (37): 277-306, 2001

      7 I. Sodagar, "The mpeg-dash standard for multimedia streaming over the internet" 18 (18): 62-67, 2011

      8 "The Apache Software Foundation"

      9 J.-S. Leu, "TRASS: A transmission rate-adapted streaming server in a wireless environment" 24 (24): 852-871, 2011

      10 V. Jacobson, "TCPDUMP public repository"

      1 YouTube, "YouTube Statistics"

      2 R. C. L. Gámez, "Wireless network delay estimation for time-sensitive applications" Autom Control Dept Tech. Univ Catalonia Catalonia Spain 2006

      3 S. Akhshabi, "What Happens When HTTP Adaptive Streaming Players Compete for Bandwidth?" 2012

      4 T. Y. Huang, "Using the Buffer to Avoid Rebuffers: Evidence from a Large Video Streaming Service"

      5 F. Wamser, "Using buffered playtime for QoE-oriented resource management of YouTube video streaming" 24 (24): 288-302, 2013

      6 L. Qiu, "Understanding the performance of many TCP flows" 37 (37): 277-306, 2001

      7 I. Sodagar, "The mpeg-dash standard for multimedia streaming over the internet" 18 (18): 62-67, 2011

      8 "The Apache Software Foundation"

      9 J.-S. Leu, "TRASS: A transmission rate-adapted streaming server in a wireless environment" 24 (24): 852-871, 2011

      10 V. Jacobson, "TCPDUMP public repository"

      11 M. Zink, "Subjective impression of variations in layer encoded videos" Springer 137-154, 2003

      12 R. C. Prim, "Shortest Connection Networks And Some Generalizations" 36 (36): 1389-1401, 1957

      13 R. Houdaille, "Shaping http adaptive streams for a better user experience" 1-9, 2012

      14 S. Akhshabi, "Server-based traffic shaping for stabilizing oscillating adaptive streaming players" 19-24, 2013

      15 N. Blagus, "Self-similar scaling of density in complex real-world net-works" 391 (391): 2794-2802, 2012

      16 J. Park, "Rate adaptation scheme for HTTP-based streaming to achieve fairness with competing TCP traffic" 222-226, 2015

      17 R Core Team, "R: A Language and Environment for Statistical Computing" R Foundation for Statistical Computing 2015

      18 X. Liu, "QoE-aware Traffic Shaping for HTTP Adaptive Streaming" 9 (9): 2014

      19 N. Bouten, "QoE-Driven In-Network Optimization for Adaptive Video Streaming Based on Packet Sampling Measure-ments" 2015

      20 K. Oida, "Propagation of Low Variability in Video Traffic" 10 (10): 448-461, 2015

      21 Z. Li, "Probe and adapt: Rate adapta-tion for http video streaming at scale" 32 (32): 719-733, 2014

      22 J. Kwapień, "Physical approach to complex systems" 116-225, 2012

      23 S. Sen, "Online smoothing of vari-able-bit-rate streaming video" 2 (2): 37-48, 2000

      24 J. Famaey, "On the merits of SVC-based HTTP adaptive streaming" 419-426, 2013

      25 S. Hemminger, "Network emulation with NetEm" 18-23, 2005

      26 A. Rao, "Network Characteris-tics of Video Streaming Traffic" 2011

      27 "Microsoft Smooth Streaming"

      28 K. Miller, "Low-Delay Adaptive Video Streaming Based on Short-Term TCP Throughput Prediction"

      29 J. Jiang, "Improving fairness, efficiency, and stability in http-based adap-tive video streaming with festive" 97-108, 2012

      30 A. Zambelli, "IIS smooth streaming technical overview" Microsoft

      31 R. N. Mantegna, "Hierarchical structure in financial markets" 11 (11): 193-197, 1999

      32 Apple, "HTTP Live Streaming Resources - Apple Developer"

      33 P. Ni, "Fine-grained scalable streaming from coarse-grained videos" 103-108, 2009

      34 J. Yao, "Empirical evaluation of HTTP adaptive streaming under vehicular mobility" 92-105, 2011

      35 K. Park, "Effect of traffic self-similarity on network performance" 296-310, 1997

      36 T. Stockhammer, "Dynamic adaptive streaming over HTTP–: standards and design principles" 133-144, 2011

      37 S. Lederer, "Dynamic adaptive streaming over HTTP dataset" 89-94, 2012

      38 T.-Y. Huang, "Downton abbey without the hiccups: Buffer-based rate adaptation for http video streaming" 9-14, 2013

      39 "Diethelm Wuertz and et all, Rmetrics"

      40 B. J. Villa, "Detecting period and burst durations in video streaming by means of active probing" 2 : 460-467, 2013

      41 G. Hooghiemstra, "Delay distributions on fixed internet paths" Delft Uni-versity of Technology 2001

      42 J. Liebeherr, "Delay bounds in communication networks with heavy-tailed and self-similar traffic" 58 (58): 1010-1024, 2012

      43 T. Y. Huang, "Confused, timid, and unstable : picking a video streaming rate is hard" 225-238, 2012

      44 "Cisco visual networking index: forecast and methodology"

      45 X. K. Zou, "Can Accurate Predictions Improve Video Streaming in Cellular Networks?" 2015

      46 T. Kupka, "An evaluation of live adaptive HTTP segment streaming request strategies" 604-612, 2011

      47 "Adobe HTTP Dynamic Streaming"

      48 K. Satoda, "Adaptive video pacing method based on the pre-diction of stochastic TCP throughput" 1944-1950, 2012

      49 Y. T. Yu, "Adaptive Transmission Control Protocol-trunking flow control mechanism for supporting proxy-assisted video on demand system" 25 (25): 1363-1380, 2012

      50 A. Botta, "A tool for the generation of realistic network workload for emerging networking scenarios" 56 (56): 3531-3547, 2012

      51 G. Cofano, "A control architecture for massive adaptive video streaming delivery" 7-12, 2014

      52 C. Müller, "A VLC media player plugin enabling dynamic adaptive streaming over HTTP" 723-726, 2011

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      학술지등록 한글명 : KSII Transactions on Internet and Information Systems
      외국어명 : KSII Transactions on Internet and Information Systems
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2013-10-01 평가 등재학술지 선정 (기타) KCI등재
      2011-01-01 평가 등재후보학술지 유지 (기타) KCI등재후보
      2009-01-01 평가 SCOPUS 등재 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.45 0.21 0.37
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.32 0.29 0.244 0.03
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